P331 - BTW2023- Datenbanksysteme für Business, Technologie und Web

Permanent URI for this collectionhttps://dl.gi.de/handle/20.500.12116/40312

Authors with most Documents  

König-Ries, Birgitta
6
Habich, Dirk
5
Lehner, Wolfgang
4
Binnig, Carsten
3
Hartmann, Claudio
3

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Subject: search.filters.subject.machine learning

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  • Conference Paper
    Workload Prediction for IoT Data Management Systems
    (Gesellschaft für Informatik e.V., 2023) Burrell, David; Chatziliadis, Xenofon; Zacharatou, Eleni Tzirita; Zeuch, Steffen; Markl, Volker; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    The Internet of Things (IoT) is an emerging technology that allows numerous devices, potentially spread over a large geographical area, to collect and collectively process data from high-speed data streams.To that end, specialized IoT data management systems (IoTDMSs) have emerged.One challenge in those systems is the collection of different metrics from devices in a central location for analysis. This analysis allows IoTDMSs to maintain an overview of the workload on different devices and to optimize their processing. However, as an IoT network comprises of many heterogeneous devices with low computation resources and limited bandwidth, collecting and sending workload metrics can cause increased latency in data processing tasks across the network.In this ongoing work, we present an approach to avoid unnecessary transmission of workload metrics by predicting CPU, memory, and network usage using machine learning (ML).Specifically, we demonstrate the performance of two ML models, linear regression and Long Short-Term Memory (LSTM) neural network, and show the features that we explored to train these models.This work is part of an ongoing research to develop a monitoring tool for our new IoTDMS named NebulaStream.
  • Conference Paper
    JumpXClass: Explainable AI for Jump Classification in Trampoline Sports
    (Gesellschaft für Informatik e.V., 2023) Woltmann, Lucas; Ferger, Katja; Hartmann, Claudio; Lehner, Wolfgang; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    Movement patterns in trampoline gymnastics have become faster and more complex with the increase in the athletes’ capabilities. This makes the assessment of jump type, pose, and quality during training or competitions by humans very difficult or even impossible. To counteract this development, data-driven solutions are thought to be a solution to improve training. In recent work, sensor measurements and machine learning is used to automatically predict jumps and give feedback to the athletes and trainers. However, machine learning models, and especially neural networks, are black boxes most of the time. Therefore, the athletes and trainers cannot gain any insights about the jump from the machine learning-based jump classification. To better understand the jump execution during training, we propose JumpXClass: a tool for automatic machine learning-based jump classification with explainable artificial intelligence. Using elements of explainable artificial intelligence can improve the training experience for athletes and trainers. This work will demonstrate a live system capable to classify and explain jumps from trampoline athletes.
  • Conference Paper
    RAPP: A Responsible Academic Performance Prediction Tool for Decision-Making in Educational Institutes
    (Gesellschaft für Informatik e.V., 2023) Duong, Manh Khoi; Dunkelau, Jannik; Cordova, José Andrés; Conrad, Stefan; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    Due to the increasing importance of educational data mining for the early intervention of at-risk students and the growth of performance data collected in educational institutes, it becomes natural to employ machine learning models to predict student's performances based off prior data. Although machine learning pipelines are often similar, developing one for a specific target prediction of academic success can become a daunting task. In this work, we present a graphical user interface which implements a customisable machine learning pipeline which allows the training and evaluation of machine learning models for different definitions of academic success, \eg, collected credits, average grade, number of passed exams, etc. The evaluation is exported in PDF format after finishing training. As this tool serves as a decision support system for socially responsible AI systems, fairness notions were included in the evaluation to detect potential discrimination in the data and prediction space.
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